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survey_analysis.Rmd
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---
title: "survey_analysis"
author: "Michael Breshock"
date: "6/18/2021"
output: html_document
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
library(tidyverse)
library(googlesheets4)
library(openxlsx)
library(magrittr)
library(ggpubr)
library(forcats)
```
*note: if time, consider remaking plots with ggpubr theme*
### Read in google form survey responses sheet:
```{r}
survey = read_sheet("https://docs.google.com/spreadsheets/d/10OBdqdZKAsXzamg-IQvlIzyGd0cS2kF-xBAKXJ0yFl0/edit#gid=385855959")
```
### removing first two empty rows and filtering for only respondents that consented
```{r}
responses = survey[-1:-2,]
# renaming a couple global columns
consented = responses %>% rename(consent = `Do you consent to your responses being used for research purposes? Please see the description at the beginning of the survey for more information (use the back button within this survey if you wish to see it again).`) %>% rename(responder_category = `How would you describe yourself (choose best option)? Note that you can say "wish not to say" for the "other" category.`) %>%
filter(consent == "Yes") %>% rename(date = Timestamp)
# cleaning up responses
consented %<>% mutate(responder_category = str_replace(responder_category, "Data analyst - using data science in the corporate world", "Self-learner") %>% str_extract(paste(c("Student","Educator","Self-learner"), collapse="|")))
# saving attribute label (original survey question)
attr(consented$date, "label") = "The date when the responder submitted the survey."
attr(consented$responder_category, "label") = 'How would you describe yourself (choose best option)? Note that you can say "wish not to say" for the "other" category.'
attr(consented$consent, "label") = "Do you consent to your responses being used for research purposes? Please see the description at the beginning of the survey for more information (use the back button within this survey if you wish to see it again)."
# split into 3 objects based on responder class (student,educator,self-learner)
# remove columns that are all NA
# possibly transpose - depends which looks best
```
### splitting data table into different respondent types and removing columns that are all NA values
```{r}
student = filter(consented, responder_category == "Student")
student = student[,colSums(is.na(student))<nrow(student)]
educator = filter(consented, responder_category == "Educator")
educator = educator[,colSums(is.na(educator))<nrow(educator)]
self_learner = filter(consented, responder_category == "Self-learner")
self_learner = self_learner[,colSums(is.na(self_learner))<nrow(self_learner)]
# other = filter(consented, !(responder_category %in% c("Student - using case studies for a course or to help with a course", "Educator of Data Science/Stats/Public Health/other - looking for data and education material to use in courses", "Self-learner - interested in learning more about the skills, topics, and or concepts covered in the case studies")))
# other = other[,colSums(is.na(other))<nrow(other)]
```
use stringr to remove unneccessary info in responses
ex: change responses to just Student, Educator, Self-learner etc -> str_extract
ex: remove "interested in" from "interested in statistics etc" responses -> str_remove
case_when() also helpful (from dplyr)
see diet case study for help
look into making codebook
see slack
https://www.alexcernat.com/easy-way-to-make-a-codebook-in-r/
## Wrangling
### changing column/variable names, saving old name (the question posed in survey) as attribute label, shortening answers
educator:
```{r educatordf}
educatordf = educator %>%
rename(future_teach =
`Are you interested in using our case study materials or the data from one of our case studies to teach in the future?`)
attr(educatordf$future_teach, "label") =
"Are you interested in using our case study materials or the data from one of our case studies to teach in the future?"
educatordf = educatordf %>%
rename(feedback1 =
`Please provide any additional feedback you might have....5`)
attr(educatordf$feedback1, "label") =
"Please provide any additional feedback you might have....5"
educatordf = educatordf %>%
rename(recommend =
`How likely are you to recommend the case studies to others?...25`)
attr(educatordf$recommend, "label") =
"How likely are you to recommend the case studies to others?...25"
educatordf = educatordf %>%
rename(teaching_interests =
`What specifically brought you to the case studies? (select all that apply)...26`)
educatordf %<>% mutate(teaching_interests = str_extract_all(teaching_interests,
paste(c("statistics", "specific statistical topic",
"data science", "specific data science skill",
"R programming", "tidyverse", "public health",
"specific public health topic"), collapse="|")))
attr(educatordf$teaching_interests, "label") =
"What specifically brought you to the case studies? (select all that apply)...26"
attr(educatordf$teaching_interests, "values") =
list("statistics" = "General interest in teaching material about statistics",
"specific statistical topic" =
"Interested in teaching about a specific statistical topic (paired t-test, regression, etc.)",
"data science" = "General interest in teaching materials about data science/data analysis",
"specific data science skill" =
"Interested in teaching about a specific data science skill (such as creating a dashboard)",
"R programming" = "General interest in teaching materials about R programming",
"tidyverse" = "General interest in teaching materials about the tidyverse",
"public health" = "General interest in teaching materials about public health",
"specific public health topic" =
"Interested in teaching about a specific public health topic (such as mental health, obesity etc.)"
)
# seperating teaching_interests into boolean columns
educatordf %<>%
mutate(
teach_stats = case_when(
grepl("statistics", teaching_interests) == TRUE ~ "1",
is.na(teaching_interests) ~ "NA",
TRUE ~ "0"),
teach_spfc_stats = case_when(
grepl("specific statistical topic", teaching_interests) == TRUE ~ "1",
is.na(teaching_interests) ~ "NA",
TRUE ~ "0"),
teach_ds = case_when(
grepl('"data science"', teaching_interests) == TRUE ~ "1",
grepl("data science$", teaching_interests) == TRUE ~ "1",
is.na(teaching_interests) ~ "NA",
TRUE ~ "0"),
teach_spfc_ds = case_when(
grepl("specific data science skill", teaching_interests) == TRUE ~ "1",
is.na(teaching_interests) ~ "NA",
TRUE ~ "0"),
teach_R = case_when(
grepl("R programming", teaching_interests) == TRUE ~ "1",
is.na(teaching_interests) ~ "NA",
TRUE ~ "0"),
teach_tidyverse = case_when(
grepl("tidyverse", teaching_interests) == TRUE ~ "1",
is.na(teaching_interests) ~ "NA",
TRUE ~ "0"),
teach_PH = case_when(
grepl('"public health"', teaching_interests) == TRUE ~ "1",
grepl("public health$", teaching_interests) == TRUE ~ "1",
is.na(teaching_interests) ~ "NA",
TRUE ~ "0"),
teach_spfc_PH = case_when(
grepl("specific public health topic", teaching_interests) == TRUE ~ "1",
is.na(teaching_interests) ~ "NA",
TRUE ~ "0")
) %>% relocate(teach_stats, teach_spfc_stats, teach_ds, teach_spfc_ds,
teach_R, teach_tidyverse, teach_PH, teach_spfc_PH,
.after = teaching_interests)
educatordf = educatordf %>%
rename(might_teach =
`Please tell us how you might teach (or already taught) with one or more of our case studies. (Example: for materials for students to practice writing about the results of a data analysis)`)
attr(educatordf$might_teach, "label") =
"Please tell us how you might teach (or already taught) with one or more of our case studies. (Example: for materials for students to practice writing about the results of a data analysis)"
educatordf = educatordf %>%
rename(type_student_taught =
`What type of students do you teach? (select all that apply)`)
attr(educatordf$type_student_taught, "label") =
"What type of students do you teach? (select all that apply)"
# seperating type_student_taught into boolean columns
educatordf %<>%
mutate(
teach_grad = case_when(
grepl("Graduate students", type_student_taught) == TRUE ~ "1",
is.na(type_student_taught) ~ "NA",
TRUE ~ "0"),
teach_ugrad = case_when(
grepl("Undergraduate students", type_student_taught) == TRUE ~ "1",
is.na(type_student_taught) ~ "NA",
TRUE ~ "0"),
teach_HS = case_when(
grepl("High school students", type_student_taught) == TRUE ~ "1",
is.na(type_student_taught) ~ "NA",
TRUE ~ "0"),
teach_middle = case_when(
grepl("Middle school students", type_student_taught) == TRUE ~ "1",
is.na(type_student_taught) ~ "NA",
TRUE ~ "0")
) %>% relocate(teach_grad, teach_ugrad, teach_HS, teach_middle,
.after = type_student_taught)
educatordf = educatordf %>%
rename(course_type =
`What type of course(s) would you (or have you) used our case studies for? (check all that apply)`)
attr(educatordf$course_type, "label") =
"What type of course(s) would you (or have you) used our case studies for? (check all that apply)"
# seperating course_type into boolean columns
educatordf %<>%
mutate(
would_use_PH_course = case_when(
grepl("Public Health", course_type) == TRUE ~ "1",
is.na(course_type) ~ "NA",
TRUE ~ "0"),
would_use_stats_course = case_when(
grepl("Statistics", course_type) == TRUE ~ "1",
is.na(course_type) ~ "NA",
TRUE ~ "0"),
would_use_math_course = case_when(
grepl("Math", course_type) == TRUE ~ "1",
is.na(course_type) ~ "NA",
TRUE ~ "0"),
would_use_DS_course = case_when(
grepl("Data Science", course_type) == TRUE ~ "1",
is.na(course_type) ~ "NA",
TRUE ~ "0")
) %>% relocate(would_use_PH_course, would_use_stats_course, would_use_math_course,
would_use_DS_course, .after = course_type)
educatordf = educatordf %>%
rename(case_studies =
`What case study(ies) have you looked at? (select all that apply)...53`)
educatordf %<>%
mutate(
case_studies = str_extract_all(case_studies,
paste(c("obesity:", "diet:", "school shootings",
"multicollinearity and RTC laws (wrangling)",
"multicollinearity and RTC laws (analysis)",
"predicting air pollution", "CO2 emissions",
"youth disconnection", "youth mental health",
"opioid shipments in the US", "None",
"vaping behaviours among US youths"), collapse = "|")),
case_studies = str_remove_all(case_studies, ":")
)
attr(educatordf$case_studies, "label") =
"What case study(ies) have you looked at? (select all that apply)...53"
# seperating case_studies into boolean columns
educatordf %<>%
mutate(
looked_at_obesity = case_when(
grepl("obesity", case_studies) == TRUE ~ "1",
is.na(case_studies) ~ "NA",
TRUE ~ "0"),
looked_at_diet = case_when(
grepl("diet", case_studies) == TRUE ~ "1",
is.na(case_studies) ~ "NA",
TRUE ~ "0"),
looked_at_school_shootings = case_when(
grepl("school shootings", case_studies) == TRUE ~ "1",
is.na(case_studies) ~ "NA",
TRUE ~ "0"),
looked_at_RTC_wrangling = case_when(
grepl("multicollinearity and RTC laws (wrangling)", case_studies,
fixed = TRUE) == TRUE ~ "1",
is.na(case_studies) ~ "NA",
TRUE ~ "0"),
looked_at_RTC_analysis = case_when(
grepl("multicollinearity and RTC laws (analysis)", case_studies,
fixed = TRUE) == TRUE ~ "1",
is.na(case_studies) ~ "NA",
TRUE ~ "0"),
looked_at_air_pollution = case_when(
grepl("predicting air pollution", case_studies) == TRUE ~ "1",
is.na(case_studies) ~ "NA",
TRUE ~ "0"),
looked_at_co2_emissions = case_when(
grepl("CO2 emissions", case_studies) == TRUE ~ "1",
is.na(case_studies) ~ "NA",
TRUE ~ "0"),
looked_at_youth_disconnection = case_when(
grepl("youth disconnection", case_studies) == TRUE ~ "1",
is.na(case_studies) ~ "NA",
TRUE ~ "0"),
looked_at_mental_health = case_when(
grepl("youth mental health", case_studies) == TRUE ~ "1",
is.na(case_studies) ~ "NA",
TRUE ~ "0"),
looked_at_opioids = case_when(
grepl("opioid shipments in the US", case_studies) == TRUE ~ "1",
is.na(case_studies) ~ "NA",
TRUE ~ "0"),
looked_at_vaping = case_when(
grepl("vaping behaviours among US youths", case_studies) == TRUE ~ "1",
is.na(case_studies) ~ "NA",
TRUE ~ "0"),
looked_at_none = case_when(
grepl("None", case_studies) == TRUE ~ "1",
is.na(case_studies) ~ "NA",
TRUE ~ "0")
) %>% relocate(looked_at_obesity, looked_at_diet, looked_at_school_shootings,
looked_at_RTC_wrangling, looked_at_RTC_analysis,
looked_at_air_pollution, looked_at_co2_emissions,
looked_at_youth_disconnection, looked_at_mental_health,
looked_at_opioids, looked_at_vaping, looked_at_none,
.after = case_studies)
educatordf = educatordf %>%
rename(ocs_interest =
`Which case study or case studies might you be interested in using. (select all that apply)`)
educatordf %<>%
mutate(ocs_interest = gsub(": .*", "", str_remove(ocs_interest, "Case study about ")))
attr(educatordf$ocs_interest, "label") =
"Which case study or case studies might you be interested in using. (select all that apply)"
# separating ocs_interest into boolean columns
educatordf %<>%
mutate(
might_use_obesity = case_when(
grepl("obesity", ocs_interest) == TRUE ~ "1",
is.na(ocs_interest) ~ "NA",
TRUE ~ "0"),
might_use_diet = case_when(
grepl("diet", ocs_interest) == TRUE ~ "1",
is.na(ocs_interest) ~ "NA",
TRUE ~ "0"),
might_use_school_shootings = case_when(
grepl("school shootings", ocs_interest) == TRUE ~ "1",
is.na(ocs_interest) ~ "NA",
TRUE ~ "0"),
might_use_RTC_wrangling = case_when(
grepl("multicollinearity and RTC laws (wrangling)", ocs_interest,
fixed = TRUE) == TRUE ~ "1",
is.na(ocs_interest) ~ "NA",
TRUE ~ "0"),
might_use_RTC_analysis = case_when(
grepl("multicollinearity and RTC laws (analysis)", ocs_interest,
fixed = TRUE) == TRUE ~ "1",
is.na(ocs_interest) ~ "NA",
TRUE ~ "0"),
might_use_air_pollution = case_when(
grepl("predicting air pollution", ocs_interest) == TRUE ~ "1",
is.na(ocs_interest) ~ "NA",
TRUE ~ "0"),
might_use_co2_emissions = case_when(
grepl("CO2 emissions", ocs_interest) == TRUE ~ "1",
is.na(ocs_interest) ~ "NA",
TRUE ~ "0"),
might_use_youth_disconnection = case_when(
grepl("youth disconnection", ocs_interest) == TRUE ~ "1",
is.na(ocs_interest) ~ "NA",
TRUE ~ "0"),
might_use_mental_health = case_when(
grepl("youth mental health", ocs_interest) == TRUE ~ "1",
is.na(ocs_interest) ~ "NA",
TRUE ~ "0"),
might_use_opioids = case_when(
grepl("opioid shipments in the US", ocs_interest) == TRUE ~ "1",
is.na(ocs_interest) ~ "NA",
TRUE ~ "0"),
might_use_vaping = case_when(
grepl("vaping behaviours among US youths", ocs_interest) == TRUE ~ "1",
is.na(ocs_interest) ~ "NA",
TRUE ~ "0"),
might_use_none = case_when(
grepl("None", ocs_interest) == TRUE ~ "1",
is.na(ocs_interest) ~ "NA",
TRUE ~ "0"),
might_use_not_sure = case_when(
grepl("Not sure yet", ocs_interest) == TRUE ~ "1",
is.na(ocs_interest) ~ "NA",
TRUE ~ "0")
) %>% relocate(might_use_obesity, might_use_diet, might_use_school_shootings,
might_use_RTC_wrangling, might_use_RTC_analysis,
might_use_air_pollution, might_use_co2_emissions,
might_use_youth_disconnection, might_use_mental_health,
might_use_opioids, might_use_vaping, might_use_none,
might_use_not_sure, .after = ocs_interest)
educatordf = educatordf %>%
rename(over18 =
`Are you 18 or over? You cannot participate in this survey if you are under 18.`)
attr(educatordf$over18, "label") =
"Are you 18 or over? You cannot participate in this survey if you are under 18."
educatordf = educatordf %>%
rename(feedback2 =
`Please provide any additional feedback you might have....76`)
attr(educatordf$feedback2, "label") =
"Please provide any additional feedback you might have....76"
educatordf = educatordf %>%
rename(parts_used =
`Are you interested in using just the data, part of case study, or a full case study?`)
educatordf %<>%
mutate(parts_used =
str_replace(parts_used, "Full case study \\(or case studies\\)", "Full") %>%
str_replace("Depends \\(sometimes just data, sometimes part or all of a case study\\)",
"Combination") %>%
str_replace("Just the data", "Data") %>%
str_replace("Only part of a case study \\(or case studies\\)", "Part"))
attr(educatordf$parts_used, "label") =
"Are you interested in using just the data, part of case study, or a full case study?"
attr(educatordf$parts_used, "values") =
list("Data" = "Just the data",
"Part" = "Only part of a case study (or case studies)",
"Full" = "Full case study (or case studies)",
"Combination" = "Depends (sometimes just data, sometimes part or all of a case study)")
# separating parts_used into boolean columns
educatordf %<>%
mutate(
interest_data = case_when(
parts_used == "Data" ~ "1",
is.na(parts_used) ~ "NA",
TRUE ~ "0"),
interest_part = case_when(
parts_used == "Part" ~ "1",
is.na(parts_used) ~ "NA",
TRUE ~ "0"),
interest_full = case_when(
parts_used == "Full" ~ "1",
is.na(parts_used) ~ "NA",
TRUE ~ "0"),
interest_combination = case_when(
parts_used == "Combination" ~ "1",
is.na(parts_used) ~ "NA",
TRUE ~ "0")
) %>% relocate(interest_data, interest_part, interest_full,
interest_combination, .after = parts_used)
educatordf = educatordf %>%
rename(materials_interest =
`Please tell us which materials you are most interested in using (or you already used to teach). (select all that apply)`)
attr(educatordf$materials_interest, "label") =
"Please tell us which materials you are most interested in using (or you already used to teach). (select all that apply)"
# separating materials_interest into boolean columns
educatordf %<>%
mutate(
motivation_interest = case_when(
grepl("Motivation/Context", materials_interest) == TRUE ~ "1",
is.na(materials_interest) ~ "NA",
TRUE ~ "0"),
import_interest = case_when(
grepl("Data Import", materials_interest) == TRUE ~ "1",
is.na(materials_interest) ~ "NA",
TRUE ~ "0"),
wrangling_interest = case_when(
grepl("Data Wrangling", materials_interest) == TRUE ~ "1",
is.na(materials_interest) ~ "NA",
TRUE ~ "0"),
exploration_interest = case_when(
grepl("Data Exploration", materials_interest) == TRUE ~ "1",
is.na(materials_interest) ~ "NA",
TRUE ~ "0"),
visualization_interest = case_when(
grepl("Data Visualization", materials_interest) == TRUE ~ "1",
is.na(materials_interest) ~ "NA",
TRUE ~ "0"),
analysis_interest = case_when(
grepl("Data Analysis", materials_interest) == TRUE ~ "1",
is.na(materials_interest) ~ "NA",
TRUE ~ "0"),
data_interest = case_when(
grepl("The data itself", materials_interest) == TRUE ~ "1",
is.na(materials_interest) ~ "NA",
TRUE ~ "0"),
all_interest = case_when(
grepl("All sections", materials_interest) == TRUE ~ "1",
is.na(materials_interest) ~ "NA",
TRUE ~ "0")
) %>% relocate(motivation_interest, import_interest, wrangling_interest,
exploration_interest, visualization_interest, analysis_interest,
data_interest, all_interest, .after = materials_interest)
educatordf = educatordf %>%
rename(data_access1 =
`Is it clear how to locate, access, and download files from a case study repository?...81`)
attr(educatordf$data_access1, "label") =
"Is it clear how to locate, access, and download files from a case study repository?...81"
educatordf = educatordf %>%
rename(already_taught =
`Have you already used our case studies or the data from our case studies to teach?`)
attr(educatordf$already_taught, "label") =
"Have you already used our case studies or the data from our case studies to teach?"
educatordf = educatordf %>%
rename(ocs_taught =
`Which case studies (or data from a case study) did you use? (select all that apply)`)
educatordf %<>%
mutate(ocs_taught = gsub(": .*", "", str_remove(ocs_taught, "Case study about ")))
attr(educatordf$ocs_taught, "label") =
"Which case studies (or data from a case study) did you use? (select all that apply)"
# separate ocs_taught into boolean columns
educatordf %<>%
mutate(
used_obesity = case_when(
grepl("obesity", ocs_taught) == TRUE ~ "1",
is.na(ocs_taught) ~ "NA",
TRUE ~ "0"),
used_diet = case_when(
grepl("diet", ocs_taught) == TRUE ~ "1",
is.na(ocs_taught) ~ "NA",
TRUE ~ "0"),
used_school_shootings = case_when(
grepl("school shootings", ocs_taught) == TRUE ~ "1",
is.na(ocs_taught) ~ "NA",
TRUE ~ "0"),
used_RTC_wrangling = case_when(
grepl("multicollinearity and RTC laws (wrangling)", ocs_taught,
fixed = TRUE) == TRUE ~ "1",
is.na(ocs_taught) ~ "NA",
TRUE ~ "0"),
used_RTC_analysis = case_when(
grepl("multicollinearity and RTC laws (analysis)", ocs_taught,
fixed = TRUE) == TRUE ~ "1",
is.na(ocs_taught) ~ "NA",
TRUE ~ "0"),
used_air_pollution = case_when(
grepl("predicting air pollution", ocs_taught) == TRUE ~ "1",
is.na(ocs_taught) ~ "NA",
TRUE ~ "0"),
used_co2_emissions = case_when(
grepl("CO2 emissions", ocs_taught) == TRUE ~ "1",
is.na(ocs_taught) ~ "NA",
TRUE ~ "0"),
used_youth_disconnection = case_when(
grepl("youth disconnection", ocs_taught) == TRUE ~ "1",
is.na(ocs_taught) ~ "NA",
TRUE ~ "0"),
used_mental_health = case_when(
grepl("youth mental health", ocs_taught) == TRUE ~ "1",
is.na(ocs_taught) ~ "NA",
TRUE ~ "0"),
used_opioids = case_when(
grepl("opioid shipments in the US", ocs_taught) == TRUE ~ "1",
is.na(ocs_taught) ~ "NA",
TRUE ~ "0"),
used_vaping = case_when(
grepl("vaping behaviours among US youths", ocs_taught) == TRUE ~ "1",
is.na(ocs_taught) ~ "NA",
TRUE ~ "0"),
used_none = case_when(
grepl("None", ocs_taught) == TRUE ~ "1",
is.na(ocs_taught) ~ "NA",
TRUE ~ "0"),
used_not_sure = case_when(
grepl("Not sure yet", ocs_taught) == TRUE ~ "1",
is.na(ocs_taught) ~ "NA",
TRUE ~ "0")
) %>% relocate(used_obesity, used_diet, used_school_shootings,
used_RTC_wrangling, used_RTC_analysis,
used_air_pollution, used_co2_emissions,
used_youth_disconnection, used_mental_health,
used_opioids, used_vaping, used_none,
used_not_sure, .after = ocs_taught)
educatordf = educatordf %>%
rename(courses_taught =
`What type of course(s) did you use our case studies or data for? (check all that apply)`)
attr(educatordf$courses_taught, "label") =
"What type of course(s) did you use our case studies or data for? (check all that apply)"
# separating courses_taught into boolean columns
educatordf %<>%
mutate(
taught_PH_course = case_when(
grepl("Public Health", courses_taught) == TRUE ~ "1",
is.na(courses_taught) ~ "NA",
TRUE ~ "0"),
taught_stats_course = case_when(
grepl("Statistics", courses_taught) == TRUE ~ "1",
is.na(courses_taught) ~ "NA",
TRUE ~ "0"),
taught_math_course = case_when(
grepl("Math", courses_taught) == TRUE ~ "1",
is.na(courses_taught) ~ "NA",
TRUE ~ "0"),
taught_DS_course = case_when(
grepl("Data Science", courses_taught) == TRUE ~ "1",
is.na(courses_taught) ~ "NA",
TRUE ~ "0")
) %>% relocate(taught_PH_course, taught_stats_course, taught_math_course,
taught_DS_course, .after = courses_taught)
educatordf = educatordf %>%
rename(data_access2 =
`Was it clear how to locate, access, and download files from a case study repository?`)
educatordf %<>%
mutate(data_access2 = str_replace(data_access2, "I found the files with a bit of difficulty",
"Not super clear"))
attr(educatordf$data_access2, "label") =
"Was it clear how to locate, access, and download files from a case study repository?"
educatordf = educatordf %>%
rename(how_taught =
`Please tell us how you taught with our data or case studies - Example: for a lecture about data wrangling or to have students write a report about a data analysis.`)
attr(educatordf$how_taught, "label") =
"Please tell us how you taught with our data or case studies - Example: for a lecture about data wrangling or to have students write a report about a data analysis."
educatordf = educatordf %>%
rename(data_save_time =
`Did using our data save you time?`)
attr(educatordf$data_save_time, "label") =
"Did using our data save you time?"
educatordf = educatordf %>%
rename(data_save_effort =
`Did using our data save you effort?`)
attr(educatordf$data_save_effort, "label") =
"Did using our data save you effort?"
educatordf = educatordf %>%
rename(teach_enjoyment =
`Compared to how you taught similar material previously, did you enjoy using the case studies to teach with?`)
attr(educatordf$teach_enjoyment, "label") =
"Compared to how you taught similar material previously, did you enjoy using the case studies to teach with?"
educatordf = educatordf %>%
rename(student_enjoyment =
`Compared to how you taught similar material previously, how well did your students seem to enjoy the case studies?`)
educatordf %<>%
mutate(student_enjoyment =
str_replace(student_enjoyment,
"My students did not enjoy learning with case study as well", "Worse") %>%
str_replace("They seemed to enjoy it about the same", "Same") %>%
str_replace("My students seemed to enjoy it better", "Better"))
attr(educatordf$student_enjoyment, "label") =
"Compared to how you taught similar material previously, how well did your students seem to enjoy the case studies?"
attr(educatordf$student_enjoyment, "values") =
list("Worse" = "My students did not enjoy learning with case study as well",
"Same" = "They seemed to enjoy it about the same",
"Better" = "My students seemed to enjoy it better")
educatordf = educatordf %>%
rename(future_data_use =
`Do you plan to use our data again in the future?`)
attr(educatordf$future_data_use, "label") =
"Do you plan to use our data again in the future?"
educatordf = educatordf %>%
rename(parts_taught =
`What did you use to teach?`)
educatordf %<>%
mutate(parts_taught =
str_replace(parts_taught, "Full case study", "Full") %>%
str_replace("Combination \\(sometimes just the data or part of the case study\\... etc\\.\\)",
"Combination") %>%
str_replace("Just the data", "Data") %>%
str_replace("Parts of a case study", "Part"))
attr(educatordf$parts_taught, "label") =
"What did you use to teach?"
attr(educatordf$parts_taught, "values") =
list("Data" = "Just the data",
"Part" = "Parts of a case study",
"Full" = "Full case study",
"Combination" = "Combination (sometimes just the data or part of the case study... etc.)")
# separating parts_taught into boolean columns
educatordf %<>%
mutate(
taught_data = case_when(
parts_taught == "Data" ~ "1",
is.na(parts_taught) ~ "NA",
TRUE ~ "0"),
taught_part = case_when(
parts_taught == "Part" ~ "1",
is.na(parts_taught) ~ "NA",
TRUE ~ "0"),
taught_full = case_when(
parts_taught == "Full" ~ "1",
is.na(parts_taught) ~ "NA",
TRUE ~ "0"),
taught_combination = case_when(
parts_taught == "Combination" ~ "1",
is.na(parts_taught) ~ "NA",
TRUE ~ "0")
) %>% relocate(taught_data, taught_part, taught_full,
taught_combination, .after = parts_taught)
educatordf = educatordf %>%
rename(modular_clarity =
`Was it clear how to use just part of the case study in a modular way?`)
attr(educatordf$modular_clarity, "label") =
"Was it clear how to use just part of the case study in a modular way?"
educatordf = educatordf %>%
rename(helpful_sections =
`Which sections were most helpful for you? (select all that apply)`)
attr(educatordf$helpful_sections, "label") =
"Which sections were most helpful for you? (select all that apply)"
# separating helpful_sections into boolean columns
educatordf %<>%
mutate(
motivation_helpful = case_when(
grepl("Motivation/Context/What are the data?", helpful_sections) == TRUE ~ "1",
is.na(helpful_sections) ~ "NA",
TRUE ~ "0"),
import_helpful = case_when(
grepl("Data Import", helpful_sections) == TRUE ~ "1",
is.na(helpful_sections) ~ "NA",
TRUE ~ "0"),
wrangling_helpful = case_when(
grepl("Data Wrangling", helpful_sections) == TRUE ~ "1",
is.na(helpful_sections) ~ "NA",
TRUE ~ "0"),
visualization_helpful = case_when(
grepl("Data Visualization", helpful_sections) == TRUE ~ "1",
is.na(helpful_sections) ~ "NA",
TRUE ~ "0"),
analysis_helpful = case_when(
grepl("Data Analysis", helpful_sections) == TRUE ~ "1",
is.na(helpful_sections) ~ "NA",
TRUE ~ "0")
) %>% relocate(motivation_helpful, import_helpful, wrangling_helpful,
visualization_helpful, analysis_helpful,
.after = helpful_sections)
educatordf = educatordf %>%
rename(do_different =
`Is there anything you would do differently next time?`)
attr(educatordf$do_different, "label") =
"Is there anything you would do differently next time?"
educatordf = educatordf %>%
rename(student_comprehension =
`Compared to how you taught similar material previously, how well did students seem to learn the content that you taught using a case study?`)
attr(educatordf$student_comprehension, "label") =
"Compared to how you taught similar material previously, how well did students seem to learn the content that you taught using a case study?"
educatordf = educatordf %>%
rename(future_cs_use =
`Do you plan to use our case studies (not just data) in the future?`)
attr(educatordf$future_cs_use, "label") =
"Do you plan to use our case studies (not just data) in the future?"
educatordf = educatordf %>%
rename(data_types =
`What type of data did you use? (select all that apply)`)
educatordf %<>%
mutate(data_types =
str_replace(data_types,
"Raw data files or original source \\(ex. a website\\)",
"Raw") %>%
str_replace("Wrangled data files \\(as rda files\\)",
"Wrangled RDA") %>%
str_replace("Wrangled data files \\(as csv files\\)",
"Wrangled CSV"))
attr(educatordf$data_types, "label") =
"What type of data did you use? (select all that apply)"
attr(educatordf$data_types, "values") =
list("Raw" = "Raw data files or original source (ex. a website)",
"Wrangled RDA" = "Wrangled data files (as rda files)",
"Wrangled CSV" = "Wrangled data files (as csv files)")
educatordf = educatordf %>%
rename(teaching_difference =
`How different was incorporating the case study or parts of the case study compared to how you typically teach similar material?`)
attr(educatordf$teaching_difference, "label") =
"How different was incorporating the case study or parts of the case study compared to how you typically teach similar material?"
educatordf = educatordf %>%
rename(cs_save_time =
`Did incorporating our case study materials into your class, save you time?`)
attr(educatordf$cs_save_time, "label") =
"Did incorporating our case study materials into your class, save you time?"
educatordf = educatordf %>%
rename(cs_save_effort =
`Did incorporating our case study materials into your class, save you effort?`)
attr(educatordf$cs_save_effort, "label") =
"Did incorporating our case study materials into your class, save you effort?"
educatordf = educatordf %>%
rename(learned_new =
`Did you learn anything new using the case studies?`)
attr(educatordf$learned_new, "label") =
"Did you learn anything new using the case studies?"
educatordf = educatordf %>%
rename(teaching_new =
`Did using our case studies allow you to incorporate something new into your teaching?`)
attr(educatordf$teaching_new, "label") =
"Did using our case studies allow you to incorporate something new into your teaching?"
educatordf = educatordf %>%
rename(biggest_benefit =
`What was the biggest benefit of using the case study?`)
attr(educatordf$biggest_benefit, "label") =
"What was the biggest benefit of using the case study?"
educatordf = educatordf %>%
rename(difficult_aspect =
`What was the most difficult aspect of using the case study?`)
attr(educatordf$difficult_aspect, "label") =
"What was the most difficult aspect of using the case study?"
educatordf = educatordf %>%
rename(typically_teach =
`How do you typically teach similar material?`)
attr(educatordf$typically_teach, "label") =
"How do you typically teach similar material?"
educatordf = educatordf %>%
rename(how_different_typical =
`If it was different from how you typically teach, how?`)
attr(educatordf$how_different_typical, "label") =
"If it was different from how you typically teach, how?"
```
checking for missed questions; if no missed questions, display the codebook
```{r educator_codebook}
educator_codebook = map_df(educatordf, function(x) attributes(x)$label) %>%
gather(key = Code, value = Label)
if(nrow(educator_codebook) == ncol(educator)){
educator_check = 1
educator_codebook
} else{
diff = ncol(educator) - nrow(educator_codebook)
educator_check = 0
print(paste("Missed", diff, "column(s)!"))
educatordf
}
```
student:
```{r studentdf}
studentdf = student %>%
rename(feedback1 =
`Please provide any additional feedback you might have....6`)
attr(studentdf$feedback1, "label") =
"Please provide any additional feedback you might have....6"
studentdf = studentdf %>%
rename(refer_back =
`How likely are you to refer back to the case study/studies in the future?...11`)
attr(studentdf$refer_back, "label") =
"How likely are you to refer back to the case study/studies in the future?...11"
studentdf = studentdf %>%
rename(famil_stats =
`How familiar were you with the statistical methods presented in the case study before you read it?...12`)
studentdf %<>%
mutate(
famil_stats = case_when(
famil_stats == "Never heard about the statistical topics covered" ~ "1",
famil_stats == "Had heard briefly about the statistical topics covered" ~ "2",
famil_stats == "Had an understanding about the topics but not as much as the case study covered"~ "3",
famil_stats == "Regularly perform the statistical methods covered/already knew all or most of the material" ~ "4",
TRUE ~ famil_stats
)
) #%>%
#mutate(famil_stats = as.numeric(famil_stats))
attr(studentdf$famil_stats, "label") = "How familiar were you with the statistical methods presented in the case study before you read it?...12"
attr(studentdf$famil_stats, "values") =
list("1" = "Never heard about the statistical topics covered",
"2" = "Had heard briefly about the statistical topics covered",
"3" = "Had an understanding about the topics but not as much as the case study covered",
"4" = "Regularly perform the statistical methods covered/already knew all or most of the material")
studentdf = studentdf %>%
rename(recommend =
`How likely are you to recommend the case studies to others?...16`)
attr(studentdf$recommend, "label") =
"How likely are you to recommend the case studies to others?...16"
studentdf = studentdf %>%
rename(famil_PH =
`How familiar were you with the public health topics presented in the case study before you read it?...19`)
studentdf %<>%
mutate(
famil_PH = case_when(
famil_PH == "Never heard about the public health topics" ~ "1",
famil_PH == "Had heard briefly about the topics" ~ "2",
famil_PH == "Had an understanding about the topics but not as much as the case study covered"~ "3",
famil_PH == "Already knew all or most of the material" ~ "4",
TRUE ~ famil_PH
)
) #%>%
#mutate(famil_PH = as.numeric(famil_PH))
attr(studentdf$famil_PH, "label") =
"How familiar were you with the public health topics presented in the case study before you read it?...19"
attr(studentdf$famil_PH, "values") =
list("1" = "Never heard about the public health topics",
"2" = "Had heard briefly about the topics",
"3" = "Had an understanding about the topics but not as much as the case study covered",
"4" = "Already knew all or most of the material")
studentdf = studentdf %>%
rename(famil_DS =
`How familiar were you with the data science topics presented in the case study before you read it?...20`)
studentdf %<>%
mutate(
famil_DS = case_when(
famil_DS == "Never heard about the data science topics" ~ "1",
famil_DS == "Had heard briefly about the topics" ~ "2",
famil_DS == "Had an understanding about the topics but not as much as the case study covered"~ "3",
famil_DS == "Already knew all or most of the material" ~ "4",
TRUE ~ famil_DS
)
) #%>%
#mutate(famil_DS = as.numeric(famil_DS))
attr(studentdf$famil_DS, "label") =
"How familiar were you with the data science topics presented in the case study before you read it?...20"
attr(studentdf$famil_DS, "values") =
list("1" = "Never heard about the data science topics",
"2" = "Had heard briefly about the topics",
"3" = "Had an understanding about the topics but not as much as the case study covered",
"4" = "Already knew all or most of the material")
studentdf = studentdf %>%
rename(learned_new =
`I learned something new about: (select all that apply)...21`)
studentdf %<>%
mutate(
learned_new = str_replace_all(learned_new, "How to implement a statistical method using R", "Rstats"),
learned_new = str_replace_all(learned_new, "A statistical method or concept", "statistics")
) %>%
mutate(
learned_new =
str_extract_all(learned_new,
paste(c("statistics","Rstats", "import", "visualization",
"wrangling", "communication", "public health",
"All of the above", "None"), collapse="|"))
)
attr(studentdf$learned_new, "label") =
"I learned something new about: (select all that apply)...21"
attr(studentdf$learned_new, "values") =
list("statistics" = "A statistical method or concept",
"Rstats" = "How to implement a statistical method using R",
"import" = "Data import methods",
"visualization" = "Data visualization methods",
"wrangling" = "Data wrangling methods",
"communication" = "Data communication methods",
"public health" = "The public health topics presented")
# separating learned_new into boolean columns
studentdf %<>%
mutate(
learned_new_stats = case_when(
grepl("statistics", learned_new) == TRUE ~ "1",
is.na(learned_new) ~ "NA",
TRUE ~ "0"),
learned_new_Rstats = case_when(
grepl("Rstats", learned_new) == TRUE ~ "1",
is.na(learned_new) ~ "NA",
TRUE ~ "0"),
learned_new_import = case_when(
grepl("import", learned_new) == TRUE ~ "1",
is.na(learned_new) ~ "NA",
TRUE ~ "0"),
learned_new_viz = case_when(
grepl("visualization", learned_new) == TRUE ~ "1",
is.na(learned_new) ~ "NA",
TRUE ~ "0"),
learned_new_wrang = case_when(
grepl("wrangling", learned_new) == TRUE ~ "1",
is.na(learned_new) ~ "NA",
TRUE ~ "0"),
learned_new_comm = case_when(
grepl("communication", learned_new) == TRUE ~ "1",
is.na(learned_new) ~ "NA",
TRUE ~ "0"),
learned_new_PH = case_when(
grepl("public health", learned_new) == TRUE ~ "1",
is.na(learned_new) ~ "NA",
TRUE ~ "0"),
learned_new_all = case_when(
grepl("All of the above", learned_new) == TRUE ~ "1",
is.na(learned_new) ~ "NA",
TRUE ~ "0"),
learned_new_none = case_when(
grepl("None", learned_new) == TRUE ~ "1",
is.na(learned_new) ~ "NA",
TRUE ~ "0")
) %>% relocate(learned_new_stats, learned_new_Rstats, learned_new_import, learned_new_viz,